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The impact of KLF2-KLF3 regulated cellular localization on exhausted T cell differentiation [CUT&Tag]

GSE296608 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2026/04/03 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 18 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
18 samples: 8 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Krüppel-like factors 2 and 3 regulate T cell exhaustion by directing T cell residency and migrationImmunity · 2026
Summary (written by the submitters)
During chronic viral infection, CD8 T cells lose effector function and acquire an exhausted phenotype, a process accompanied by changes in migratory behavior and tissue residency. Exhausted CD8 T cells differentiate from stem-like progenitor (Tpro) cells into either circulating, cytotoxic effector-like (Teff) cells or tissue-resident terminally exhausted (Texh) cells.
Read the full summary ↓
However, whether these migratory changes passively reflect cell state or actively influence exhaustion trajectories remains unclear. Here, we identify a transcriptional circuit involving Krüppel-like factor 2 (KLF2) and its homolog KLF3 that links migratory program to CD8 T cell differentiation during exhaustion. Using multi-module approaches, we show that KLF2 primarily promotes the expression and chromatin accessibility of migratory genes in Tpro and Teff subsets, while KLF3 limits these programs by repressing Klf2 transcription and competing for shared genomic binding sites. Enhancing circulation through enforced S1PR1/S1PR5 expression or CD69 deletion biases differentiation toward effector-like fates and away from terminal exhaustion, suggesting that cellular localization can influence, not just reflect, CD8 T cell fate decisions. We further describe an integrated feed-forward and feedback loop in which KLF2 induces KLF3, which in turn constrains KLF2 transcription and binding to shared genomic loci. This reciprocal regulatory circuit modulates the balance between migration and tissue residency, thereby tuning the differentiation of CD8 T cells in chronic infection. These findings provide mechanistic insight into how transcriptional regulation of migratory behavior contributes to cell fate decisions during T cell exhaustion.
Published in
Krüppel-like factors 2 and 3 regulate T cell exhaustion by directing T cell residency and migration
Shen J, Brown RJ, Zhang Y et al. · Immunity 2026 · PMID 41903548 · doi:10.1016/j.immuni.2026.02.021
This dataset
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Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE296608_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1260050 and SRA study SRP583696. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41903548) ↗
Study design
8 conditions, mostly in duplicate
LCMV Clone 13, Day8, P14, aKLF2, ×3 Tamoxifen 0uM, CD8, aERKLF3, ×2 Tamoxifen 2uM, CD8, aERKLF3, ×2 Tamoxifen 200uM, CD8, aERKLF3, ×2 Tamoxifen 0uM, CD8, aKLF2, ×2 Tamoxifen 2uM, CD8, aKLF2, ×2 Tamoxifen 200uM, CD8, aKLF2, ×2 Tamoxifen, CD8, aIgG, ×2 +1 more

Supports a between-group comparison across 17 samples.

How we read this: 8 replicated groups read from 18 sample titles; they account for 17 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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